Transport Capacity Of Networks Research Articles

Modeling the α-max capacity of transportation networks: a single-level mathematical programming formulation

Network capacity, defined as the largest sum of origin–destination (O–D) flows that can be accommodated by the network based on link performance function and traffic equilibrium assignment, is a critical indicator of network-wide performance assessment in transportation planning and management. The typical modeling rationale of estimating network capacity is to formulate it as a mathematical programming (MP), and there are two main approaches: single-level MP formulation and bi-level programming (BLP) formulation. Although single-level MP is readily solvable, it treats the transportation network as a physical network without considering level of service (LOS). Albeit BLP explicitly models the capacity and link LOS, solving BLP in large-scale networks is challenging due to its non-convexity. Moreover, the inconsideration of trip LOS makes the existing models difficult to differentiate network capacity under various traffic states and to capture the impact of emerging trip-oriented technologies. Therefore, this paper proposes the α-max capacity model to estimate the maximum network capacity under trip or O–D LOS requirement α. The proposed model improves the existing models on three aspects: (a) it considers trip LOS, which can flexibly estimate the network capacity ranging from zero to the physical capacity including reserve, practical and ultimate capacities; (b) trip LOS can intuitively reflect users’ maximum acceptable O–D travel time or planners’ requirement of O–D travel time; and (c) it is a convex and tractable single-level MP. For practical use, we develop a modified gradient projection solution algorithm with soft constraint technique, and provide methods to obtain discrete trip LOS and network capacity under representative traffic states. Numerical examples are presented to demonstrate the features of the proposed model as well as the solution algorithm.

Sap Flow Disruption in Grapevine Is the Early Signal Predicting the Structural, Functional, and Genetic Responses to Esca Disease.

Fungal species involved in Esca cause the formation of grapevine wood necroses. It results in the deterioration of vascular network transport capacity and the disturbance of the physiological processes, leading to gradual or sudden grapevine death. Herein, for two consecutive growing seasons, a detailed analysis of the structural (wood necrosis and leaf discoloration) and physiological parameters related to the water use of healthy and esca-symptomatic grapevines was conducted. Measurements were carried out on 17-year-old grapevines that expressed, or not, Esca-leaf symptoms in a vineyard of the Bordeaux region (France). Whole-plant transpiration was recorded continuously from pre-veraison to harvest, using noninvasive sap flow sensors. Whole-plant transpiration was systematically about 40–50% lower in Esca-diseased grapevines compared with controls, and this difference can be observed around 2 weeks before the first Esca-foliar symptoms appeared in the vineyard. Unlike grapevine sap flow disruption, structural (e.g., leaf discolorations), functional (e.g., stomatal conductance, photosynthetic activity, phenolic compounds), and genetic (e.g., expression of leaf-targeted genes) plant responses were only significantly impacted by Esca at the onset and during leaf symptoms development. We conclude that sap flow dynamic, which was related to a high level of a white-rot necrosis, provides a useful tool to predict plant disorders due to Esca-grapevine disease.

Study of the influence of autonomous vehicles on transport network capacity

Study of the influence of autonomous vehicles on transport network capacity

Multi-Criteria Decision Making Process in Metropolitan Transport Means Selection Based on the Sharing Mobility Idea

The article presents the idea of modeling the decision-making process in the field of the metropolitan areas transport system. Due to the increasing process of metropolization and urbanization, which is predicted to be 68.4% worldwide and 83.7% in Europe in 2050, the issue will be even more sophisticated. The problem of depletion of transport network capacity as well as the implementation of modern technology solutions forces metropolitan committees to apply tools for metropolitan passenger transport system optimization. Significantly, the policy and regulations on sustainable urban mobility management are based on the mobility demand predictions and understanding of the travel decision-making process of citizens. The scientific purpose of this article is to build a mathematical model, as a tool supporting the multi-criteria decision-making process regarding the choice of means of transport in a developing metropolis. The issue raised in this article considers the most important research areas of the metropolitan transport means selection, which includes transport safety, qualitative, financial, and ecological aspects. The model was implemented in Silesian Metropolis in Poland with a particular emphasis on sharing mobility transport means users. As a result, a ranking of sharing transport means was developed, which is a piece of significant information for planners and future investors in the development of the metropolitan transport system.

Optimization of Marine Port Logistics Collection and Distribution Network: A Perspective of Supply Chain Management

Yang, J. and Guo, L., 2020. Optimization of marine port logistics collection and distribution network: A perspective of supply chain management. In: Gong, D.; Zhang, M., and Liu, R. (eds.), Advances in Coastal Research: Engineering, Industry, Economy, and Sustainable Development. Journal of Coastal Research, Special Issue No. 106, pp. 473–476. Coconut Creek (Florida), ISSN 0749-0208.With the prosperity of financial trade, marine port logistics have become an important part of international logistics, which needs to improve port efficiency by optimizing the collection and distribution network. A collection and distribution network is a kind of system that integrates multiple transportation modes, including railway, highway, waterway, air transport, pipeline, etc. Zhoushan Port in Ningbo is the largest port in China. With the gradual increase in the importance of marine port logistics, the original collection and distribution network has been unable to meet the rapid growth of port cargo throughput. Therefore, the marine port's collection and distribution network system needs to be optimized based on the supply chain. This paper presents an analysis of the optimization of Zhoushan Port's collection and distribution network based on the supply chain. First, this paper forecasts the cargo throughput and container throughput of Zhoushan Port using the Grey method. Then, this paper predicts the capacity of different transportation networks. Finally, some suggestions are put forward.

Minimizing Inter-Core Crosstalk Jointly in Spatial, Frequency, and Time Domains for Scheduled Lightpath Demands in Multi-Core Fiber-based Elastic Optical Network

Elastic optical networks (EON) technology in combination with space division multiplexing (SDM) is considered as having the potential to expand the transmission capacity of optical transport networks. However, inter-core crosstalk may cause serious signal impairment in a multi-core fiber (MCF) links. At the same time, scheduled lightpath demands, for which the expected setup and teardown times are known in advance, are considered as an important type of traffic demand for future networks. In this article, we develop approaches to schedule simultaneous lightpaths onto non-adjacent MCF cores so as to reduce inter-core crosstalk between these lightpaths. To this end, we first define a new metric to estimate the inter-core crosstalk jointly considering the spatial, frequency, and time domains. We then tackle the routing, spectrum, core, and time assignment (RSCTA) problem for the MCF-based EON by developing an integer linear programming (ILP) model, as well as an auxiliary graph (AG) based heuristic algorithm, which jointly optimize spectrum resource utilization and reduce the lightpath inter-core crosstalk. Simulation studies show the effectiveness of the proposed approach in terms of both performance aspects. In addition, the performance of the proposed heuristic algorithm is shown to be close to that of the ILP model in small networks.

Analysis of the Passenger Flow Transfer Capacity of a Bus-Subway Transfer Hub in an Urban Multi-Mode Transportation Network

In the context of a rapid developing urban economy and the increasing number of motor vehicles, urban commuting transportation has witnessed a serious mismatch between the supply of and the demand for transportation network resources. In developing an urban multi-mode traffic network, using a urban traffic transfer hub to coordinate the transportation capabilities among different traffic networks is perceived to be highly effective for exploring the network transportation capacity of an entire transportation system, and improving travel efficiency and experiences for the public. Based on the super-network model, this paper develops a topological structure for a multi-mode traffic network, in which two typical combined travel modes are selected to establish the path impedance function for that network. Moreover, the multi-mode traffic allocation model and the solving algorithm are constructed in the research. The paper studies the impact of two types of factors related to the transfer capacity of passenger flows based on the regular traffic network of a bus-and-subway transfer hub using a sensitivity analysis of the transfer time and the associated penalty. The findings suggest that both changes in transfer walking time and the transfer penalty have no significant effect on the bus passenger flow.

The effect of low water on loading capacity of inland ships

Temporary low water levels can have a major impact on the loading capacity of inland ships, and as a consequence on the transport capacity of the overall waterborne supply chain. Insight in the capacity reducing effect of temporarily lowered water levels is important for the design and operation of robust transport chains on the one hand, and for the optimisation of fairway maintenance and long-term infrastructure development on the other. Knowledge on the effects of low water is clearly available at the level of individual ship owners, who adapt their transport operations to changing environmental circumstances, but less accessible at an aggregated level to assess the effects on the overall transport capacity of an inland waterway network. Based on a range of field observations and information collected from individual ships, this article introduces a general model to define the effect of low water constraints on the deadweight capacity and payload of inland ships, for which only the type, length, and beam of the vessel serve as input.

Economic efficiency of strategies that change multimodal transportation network shape and capacity

The methodology developed by the authors for designing a stage-by-stage change the shape and capacity of a multimodal transportation network allows, on the basis of the initial set of strategies, forming the area of effective strategies. This area is used to make an optimal design decision. The article discusses the efficiency of the strategy that stage-by-stage changes shape and capacity of the multimodal transportation network. In this study the strategy efficiency is the strategy compliance with the set goals and interests of potential participants of the investment project for its implementation. At the same time, the use of only economic indicators does not always determine the optimal strategy. For this purpose, in the multimodal transportation network designing methodology a set of interrelated balanced indicators is applied: economic, social, technical, operational, environmental, etc. The article provides recommendations for determining the indicators of economic efficiency: social, national (federal), regional and industry efficiencies included in the Balanced scorecard. These indicators allow estimating possible outcomes of strategies implementation of multimodal transport network development in economic and social spheres for society as a whole, economy of the country, transport industry, and also investment appeal of strategy for potential participants of the project on its realization.

Aspect of pedestrian safety at road intersections with traffic lights

The Processes of Globalisation, mobility of the society, modernization of transport infrastructure, the desire to own means of transport contribute to the increased number of road vehicles and many transport problems. These include, above all, the occurrence of transport congestion, which in turn is the source of further problems of road transport systems. Environmental pollution, increased noise, occurring traffic hazards, difficult communication has a negative impact on the functioning of users of urban transport systems. In contrast to the increasing expectations of users of urban transport systems, systems supporting their safety and increasing the capacity of transport networks come out. One of the examples of the system is traffic lights. The implementation of this system allows for safer movement of individual road users, including pedestrians. The article discusses the problem of pedestrian safety in road traffic. The analysis of police statistical data on the number of accidents and their effects with the participation of pedestrians was presented. In addition, the results of research on the subjective assessment of pedestrian safety at road intersections using traffic lights are present-ed. The method of direct interview involving pedestrians as participants of road traffic was used.

Deep Autoencoder Neural Networks for Short-Term Traffic Congestion Prediction of Transportation Networks.

Traffic congestion prediction is critical for implementing intelligent transportation systems for improving the efficiency and capacity of transportation networks. However, despite its importance, traffic congestion prediction is severely less investigated compared to traffic flow prediction, which is partially due to the severe lack of large-scale high-quality traffic congestion data and advanced algorithms. This paper proposes an accessible and general workflow to acquire large-scale traffic congestion data and to create traffic congestion datasets based on image analysis. With this workflow we create a dataset named Seattle Area Traffic Congestion Status (SATCS) based on traffic congestion map snapshots from a publicly available online traffic service provider Washington State Department of Transportation. We then propose a deep autoencoder-based neural network model with symmetrical layers for the encoder and the decoder to learn temporal correlations of a transportation network and predicting traffic congestion. Our experimental results on the SATCS dataset show that the proposed DCPN model can efficiently and effectively learn temporal relationships of congestion levels of the transportation network for traffic congestion forecasting. Our method outperforms two other state-of-the-art neural network models in prediction performance, generalization capability, and computation efficiency.

Resilient BBU placement in 5G C-RAN over optical aggregation networks

The huge data demand envisioned for the 5G requires radical changes in the mobile network architecture and technology. Centralized radio access network (C-RAN) is introduced as a novel mobile network architecture, designed to effectively support the challenging requirements of the future 5G mobile networks. In C-RAN, BaseBand Units (BBUs) are physically separated from their corresponding radio remote heads (RRHs) and located in a central single physical location called BBU pool. The RRHs are connected to the BBU pool via the so-called fronthaul network. The “centralization” demonstrates remarkable benefits in terms of computational resources as well as power savings. Following this centralization, designing a survivable C-RAN becomes crucial as BBU pool and link failures might cause service outage for large number of users. In this paper, we propose three different approaches for the survivable BBU pool placement problem and traffic routing in C-RAN deployment over a 5G optical aggregation network. Namely, we define the following protection scenarios: (1) dedicated path protection, (2) dedicated BBU protection and (3) dedicated BBU and path protection. The three approaches are formalized as integer linear programming (ILP) problems. The ILPs objectives are to minimize the number of BBU pools, the number of used wavelengths and the baseband processing computational resources, in terms of giga operations per second. We provide numerical results to compare the aforementioned protection strategies considering different network topologies. The results show the effect of the latency and the transport–network capacity on the BBU placement. We show the trade-off between the centralization degree and the tight latency requirements. Moreover, we discuss important insights about considering the different objective functions for each protection approach.

Using a multi-objective programming model to validate feasibility of an underground freight transportation system for the Yangshan port in Shanghai

Preventing environmental deterioration and alleviating traffic congestion are becoming urgent problems in urban and transportation planning. Alleviating the pressure from increasing freight transportation traffic via low-emission and innovative transportation methods can reduce problems such as transportation network capacity limits and environmental pollution and contribute to the development of resource-efficient and sustainable cities in the future. Underground freight transportation systems (UFTSs) can improve service quality and transportation efficiency in urban logistics and alleviate traffic congestion and associated problems such as energy consumption and air pollution. Previous studies on urban UFTSs have focused mostly on technical feasibility and policy requirements. Studies providing a quantitative analysis of the effects of introducing a UFTS on an existing transportation network are scarce. In this study, the main goal is to create a quantitative method to analyze the effects of introducing a UFTS on the performance of a transportation network. Thus, a multi-objective programming model of an integrated aboveground-underground transportation network that considers transportation cost, time, and emissions is created. The Yangshan port in Shanghai, China is used as an example to assess whether a UFTS can significantly reduce the cost, time, distance and emissions of the aboveground freight container transportation. The weights on three objective functions are varied to analyze their effects on the solution. These results provide a reference for optimizing freight distribution plans when a UFTS is constructed and for implementing integrated aboveground-underground transportation systems in the future.

Regional labor markets, commuting, and the economic impact of road pricing

Cities provide the benefits that come with economic density but often face congested traffic and high unemployment. In this paper, we study economy-wide and distributional implications of congestion pricing in the presence of agglomeration externalities and unemployment. We develop a spatial general equilibrium model to show that indirect effects of time-invariant congestion tolls can lead to welfare losses for low-skilled urban residents by changing commuting patterns of high-skilled workers. Next, we reveal a set of policy designs that improve welfare across space and worker skill levels by combining time-sensitive road pricing, transport network capacity expansions, and toll revenue redistribution.

Improving the throughput of transportation networks with a time-optimization routing strategy

ABSTRACTTraffic congestion, a common and complicated phenomenon in urban transportation systems, is attracting increasing attention from researchers in Geographical Information Science (GIS) and other fields. In this study, we illustrate a general mechanism that reveals the relationship between travel time and dynamic traffic conditions. We measure a vehicle’s travel time to its destination along any path, where the travel time is calculated based on the path length and on the real-time traffic volume and transport capacity of each road segment on the path. On the basis of this measurement, we present a simple dynamic routing strategy that allows each vehicle to dynamically choose the path to its destination while imposing the minimum travel time. The application of our routing algorithm to the Chengdu street network, Barabási–Albert scale-free network and Erdös–Rényi random network shows that the proposed strategy remarkably improves network throughput and balances traffic load distribution. Our findings suggest that mining the time mechanism of network transport is important to explore efficient time-optimization routing algorithms to enhance the transport capacity of urban street networks and other kinds of networks.

Adaptive Modulation and Flexible Resource Allocation in Space-Division-Multiplexed Elastic Optical Networks

Space division multiplexing (SDM) and elastic optical networking (EON) have been proposed to increase the transmission capacity and flexibility of optical transport networks. The problem of allocating available resources over the EON network is called routing and spectrum assignment (RSA); it is called routing, modulation level, and spectrum assignment (RMLSA) if modulation adaptivity is enabled. Considering SDM adds more flexibility to the resource allocation problem. In this paper, we formulate the routing, modulation level, space, and spectrum assignment (RMLSSA) as integer linear programming (ILP) in a path-based manner for static traffic. Next, the stepwise greedy algorithm (SGA) and four different sorting policies to initiate the algorithmare proposed as a heuristic method to find a near-optimal solution of the RMLSSA problem. Finally, the paper evaluates the effectiveness of sorting policies and the SGA algorithm with different metrics. The performance results show that the SGA initiated with connection demands sorted based on increasing the number of possible space and spectrum assignment layouts (SAL) can achieve the best near-optimal solution for a small network experiment. Moreover, the ascending SAL number (ASN) policy shows the best performance for realistic networks.

Decoding network patterns for urban disaster prevention by comparing Neihu district of Taipei and Sumida district of Tokyo

In this study, we performed routes network transport and emergency shelters capacity rate analyses to determine the accessibility and efficacy of urban patterns, and established a quantitative method for supplying priorities for actions of "Sendai Framework for Disaster Risk Reduction 2015-2030". By comparing two case studies, we used Space Syntax to develop two important indicators, Rn and CR, to present geographic information and hazard risk in a physical environment. This research also found potential function of Rn and decoded some patterns for urban planners or decision makers as follows：The most efficient configuration of the road network was not in the old areas of these two case studies because the several turns decreased the connectivity of the networks. And the CR indicator shown other findings about the quality of public facilities and services as follows：The service capacity of the emergency shelters was surveyed to indicate a higher correlation of residents population and preparedness security for disaster management. Therefore, with finding some risks that had not been encountered before, we addressed this proposed method is feasible and reliable to enhance the disaster preparedness for action regarding the 4th priority of “Sendai Framework for Disaster Risk Reduction 2015-2030”.

Effective hybrid link-adding strategy to enhance network transport efficiency for scale-free networks

In this paper, we propose a new strategy (HLS) to enhance the transport capacity of scale-free networks by adding links to the existing networks, based on the betweenness of nodes, the shortest path length and the betweenness of links. Since only slight amounts of nodes in scale-free networks have high betweenness centrality, local link-adding strategy is adopted as a part of HLS for target nodes, which can significantly reduce the load of target nodes. Moreover, in order to improve the robustness of our strategy under some extreme cases, second sorting procedure is introduced in HLS. Simulation results show that HLS outperforms the IE strategy in terms of transport capacity and delivering ability of scale-free networks. After the adding links process of HLS, the congestion can be alleviated efficiently, which is meaningful to the realistic networks.

5G transport network requirements for the next generation fronthaul interface

To meet the requirements of 5G mobile networks, several radio access technologies, such as millimeter wave communications and massive MIMO, are being proposed. In addition, cloud radio access network (C-RAN) architectures are considered instrumental to fully exploit the capabilities of future 5G RANs. However, RAN centralization imposes stringent requirements on the transport network, which today are addressed with purpose-specific and expensive fronthaul links. As the demands on future access networks rise, so will the challenges in the fronthaul and backhaul segments. It is hence of fundamental importance to consider the design of transport networks alongside the definition of future access technologies to avoid the transport becoming a bottleneck. Therefore, we analyze in this work the impact that future RAN technologies will have on the transport network and on the design of the next generation fronthaul interface. To understand the especially important impact of varying user traffic, we utilize measurements from a real-world 4G network and, taking target 5G performance figures into account, extrapolate its statistics to a 5G scenario. With this, we derive both per-cell and aggregated data rate requirements for 5G transport networks. In addition, we show that the effect of statistical multiplexing is an important factor to reduce transport network capacity requirements and costs. Based on our investigations, we provide guidelines for the development of the 5G transport network architecture.

FRACTAL CHARACTERIZATION OF DYNAMIC FRACTURE NETWORK EXTENSION IN POROUS MEDIA

Fracture network and fractured porous media as well as their transport properties have received great attentions in many fields from engineering application and basic theoretical researches. Fracture will dynamically extend in length and aperture to form complex fracture network under some external conditions such as percussion drilling, wave propagation, desiccation and hydrofracturing. The complexity of fracture network can be well quantitatively characterized by fractal dimension. In this work, the dynamic characterization of fracture network extension in porous media under drying process is measured by the improved box-counting technique, and fractal dimensions of fracture network are respectively related to drying time, average aperture, moisture content and fracture porosity. The fractal dimension increases exponentially with drying time and average aperture, and decreases with moisture content in the form of power law. Specially, the fractal dimension is approximatively increased with porosity in the form of linearity in a narrow porosity range. The transport capacity of fracture network, described by seepage coefficient, is also related to the fractal dimension with drying time in the form of exponential function. The presented fractal analysis of fracture network could also shed light on the hydrofracturing application in subsurface unconventional oil and gas reservoirs.